Instructions:
Start here if this is your first time using dplyr. You’ll learn the
basic philosophy, the most important data manipulation
verbs, and the pipe, %>%, which allows
you to combine multiple verbs together to solve real problems.
Read the text and execute each chunk as you go along. If you make any
discoveries or have questions, put them into the text immediately after
the chunk/text where your doubt is created. I have put in small
instructions here and there for you to prompt your thinking and
connection-making.
When working with data you must:
The dplyr package makes these steps fast and easy:
By constraining your options, it helps you think about your data
manipulation challenges.
It provides simple “verbs”, functions that
correspond to the most common data manipulation tasks, to help you
translate your thoughts into code.
It uses efficient backends, so you spend less time waiting for
the computer.
Ne’er you mind about backends ;-) See Shakespeare’s Hamlet.
This document introduces you to dplyr’s basic set of tools, and shows
you how to apply them to data frames. dplyr also supports
databases via the dbplyr package, once you’ve installed,
read vignette("dbplyr") to learn more.
Data: starwars
To explore the basic data manipulation verbs of dplyr,
we’ll use the dataset starwars. This dataset contains 87
characters and comes from the Star Wars
API, and is documented in ?starwars
This means: type ?starwars in the Console. Try.
dim(starwars)
#> [1] 87 14
starwars
Note that starwars is a tibble, a modern
reimagining of the data frame. It’s particularly useful for large
datasets because it only prints the first few rows. You can learn more
about tibbles at https://tibble.tidyverse.org; in particular you can
convert data frames to tibbles with as_tibble().
Check your Environment Tab to inspect starwars in a
separate tab.
Single table
verbs
dplyr aims to provide a function for each basic verb of
data manipulation. These verbs can be organised into three categories
based on the component of the dataset that they work with:
- Rows:
filter() chooses rows based on column values.
slice() chooses rows based on location.
arrange() changes the order of the rows.
- Columns:
select() changes whether or not a column is
included.
rename() changes the name of columns.
mutate() changes the values of columns and creates new
columns.
relocate() changes the order of the columns.
- Groups of rows:
summarise() collapses a group into a single row.
Think of the parallels from Microsoft Excel.
The pipe
All of the dplyr functions take a data frame (or
tibble) as the first argument. Rather than forcing the user
to either save intermediate objects or nest functions, dplyr provides
the %>% operator from magrittr.
x %>% f(y) turns into f(x, y) so the result
from one step is then “piped” into the next step. You can use the pipe
to rewrite multiple operations that you can read left-to-right,
top-to-bottom (reading the pipe operator as
“then”).
Filter rows with
filter()
filter() allows you to select a subset of rows in a data
frame. Like all single verbs, the first argument is the tibble (or data
frame). The second and subsequent arguments refer to variables within
that data frame, selecting rows where the expression is
TRUE.
For example, we can select all character with light skin color and
brown eyes with:
Note the double equal to sign (==) below! Equivalent to MS Excel Data
-> Filter
starwars %>% filter(skin_color == "light", eye_color == "brown")
Arrange rows with
arrange()
arrange() works similarly to filter()
except that instead of filtering or selecting rows, it
reorders them. It takes a data frame, and a set of
column names (or more complicated expressions) to order by. If you
provide more than one column name, each additional column will be used
to break ties in the values of preceding columns:
starwars %>% arrange(height, mass)
Use desc() to order a column in descending order:
starwars %>% arrange(desc(height))
Choose rows using
their position with slice()
slice() lets you index rows by their (integer)
locations. It allows you to select, remove, and duplicate rows.
This is an important step in Prediction, Modelling and Machine
Learning.
We can get characters from row numbers 5 through 10.
starwars %>% slice(5:10)
It is accompanied by a number of helpers for common use cases:
slice_head() and slice_tail() select the
first or last rows.
starwars %>% slice_head(n = 3)
slice_sample() randomly selects rows. Use the option
prop to choose a certain proportion of the cases.
starwars %>% slice_sample(n = 5)
starwars %>% slice_sample(prop = 0.1)
Use replace = TRUE to perform a bootstrap sample. If
needed, you can weight the sample with the weight
argument.
Bootstrap samples are a special statistical sampling
method. Counterintuitive perhaps, since you sample with replacement.
Should remind you of your high school Permutation and Combination class,
with all those urn models and so on. If you remember.
slice_min() and slice_max() select rows
with highest or lowest values of a variable. Note that we first must
choose only the values which are not NA.
starwars %>%
filter(!is.na(height)) %>%
slice_min(height, n = 3)
Select columns with
select()
Often you work with large datasets with many columns but only a few
are actually of interest to you. select() allows you to
rapidly zoom in on a useful subset using operations that usually only
work on numeric variable positions:
# Select columns by name
starwars %>% select(hair_color, skin_color, eye_color)
# Select all columns between hair_color and eye_color (inclusive)
starwars %>% select(hair_color:eye_color)
# Select all columns except those from hair_color to eye_color (inclusive)
starwars %>% select(!(hair_color:eye_color))
# Select all columns ending with color
starwars %>% select(ends_with("color"))
There are a number of helper functions you can use within
select(), like starts_with(),
ends_with(), matches() and
contains(). These let you quickly match larger blocks of
variables that meet some criterion. See ?select for more
details.
You can rename variables with select() by using named
arguments:
starwars %>% select(home_world = homeworld)
But because select() drops all the variables not
explicitly mentioned, it’s not that useful. Instead, use
rename():
starwars %>% rename(home_world = homeworld)
Add new columns
with mutate()
Besides selecting sets of existing columns, it’s often useful to add
new columns that are functions of existing columns. This is the job of
mutate():
starwars %>% mutate(height_m = height / 100)
We can’t see the height in meters we just calculated, but we can fix
that using a select command.
starwars %>%
mutate(height_m = height / 100) %>%
select(height_m, height, everything())
dplyr::mutate() is similar to the base
transform(), but allows you to refer to columns that you’ve
just created:
starwars %>%
mutate(
height_m = height / 100,
BMI = mass / (height_m^2)
) %>%
select(BMI, everything())
If you only want to keep the new variables, use
transmute():
starwars %>%
transmute(
height_m = height / 100,
BMI = mass / (height_m^2)
)
Change column order
with relocate()
Use a similar syntax as select() to move blocks of
columns at once
starwars %>% relocate(sex:homeworld, .before = height)
Summarise values
with summarise()
The last verb is summarise(). It collapses a data frame
to a single row.
starwars %>% summarise(mean_height = mean(height, na.rm = TRUE))
It’s not that useful until we learn the group_by() verb
below.
Commonalities
You may have noticed that the syntax and function of all these verbs
are very similar:
The first argument is a data frame.
The subsequent arguments describe what to do with the data frame.
You can refer to columns in the data frame directly without using
$.
The result is a new data frame
Together these properties make it easy to chain together multiple
simple steps to achieve a complex result.
These five functions provide the basis of a language of data
manipulation. At the most basic level, you can only alter a tidy data
frame in five useful ways: you can reorder the rows
(arrange()), pick observations and variables of interest
(filter() and select()), add new variables
that are functions of existing variables (mutate()), or
collapse many values to a summary (summarise()).
Combining functions
with %>%
The dplyr API is functional in the sense that function calls don’t
have side-effects. You must always save their results. This doesn’t lead
to particularly elegant code, especially if you want to do many
operations at once. You either have to do it step-by-step:
a1 <- group_by(starwars, species, sex)
a2 <- select(a1, height, mass)
a3 <- summarise(a2,
height = mean(height, na.rm = TRUE),
mass = mean(mass, na.rm = TRUE)
)
Or if you don’t want to name the intermediate results, you need to
wrap the function calls inside each other:
summarise(
select(
group_by(starwars, species, sex),
height, mass
),
height = mean(height, na.rm = TRUE),
mass = mean(mass, na.rm = TRUE)
)
#> Adding missing grouping variables: `species`, `sex`
#> `summarise()` has grouped output by 'species'. You can override using the `.groups` argument.
This is difficult to read because the order of the operations is from
inside to out. Thus, the arguments are a long way away from the
function. To get around this problem, dplyr provides the
%>% operator from magrittr. x %>% f(y)
turns into f(x, y) so you can use it to rewrite multiple
operations that you can read left-to-right, top-to-bottom (reading the
pipe operator as “then”):
starwars %>%
group_by(species, sex) %>%
summarise(
mean_height = mean(height, na.rm = TRUE),
mean_mass = mean(mass, na.rm = TRUE)
)
#> `summarise()` has grouped output by 'species'. You can override using the `.groups` argument.
Patterns of
operations
The dplyr verbs can be classified by the type of operations they
accomplish (we sometimes speak of their semantics,
i.e., their meaning). It’s helpful to have a good grasp of the
difference between select and mutate operations.
Selecting
operations
One of the appealing features of dplyr is that you can refer to
columns from the tibble as if they were regular variables. However, the
syntactic uniformity of referring to bare column names hides semantical
differences across the verbs. A column symbol supplied to
select() does not have the same meaning as the same symbol
supplied to mutate().
Selecting operations expect column names and positions. Hence, when
you call select() with bare variable names, they actually
represent their own positions in the tibble. The following calls are
completely equivalent from dplyr’s point of view:
# `name` represents the integer 1
select(starwars, name)
select(starwars, 1)
By the same token, this means that you cannot refer to variables from
the surrounding context if they have the same name as one of the
columns. In the following example, height still represents
2, not 5:
height <- 5
select(starwars, height)
One useful subtlety is that this only applies to bare names and to
selecting calls like c(height, mass) or
height:mass. In all other cases, the columns of the data
frame are not put in scope. This allows you to refer to contextual
variables in selection helpers:
name <- "color"
select(starwars, ends_with(name))
These semantics are usually intuitive. But note the subtle
difference:
name <- 5
select(starwars, name, identity(name))
In the first argument, name represents its own position
1. In the second argument, name is evaluated
in the surrounding context and represents the fifth column.
Mutating
operations
Mutate semantics are quite different from selection semantics.
Whereas select() expects column names or positions,
mutate() expects column vectors. We will set up a
smaller tibble to use for our examples.
df <- starwars %>% select(name, height, mass)
When we use select(), the bare column names stand for
their own positions in the tibble. For mutate() on the
other hand, column symbols represent the actual column vectors stored in
the tibble. Consider what happens if we give a string or a number to
mutate():
mutate(df, "height", 2)
mutate() gets length-1 vectors that it interprets as new
columns in the data frame. These vectors are recycled so they match the
number of rows. That’s why it doesn’t make sense to supply expressions
like "height" + 10 to mutate(). This amounts
to adding 10 to a string! The correct expression is:
mutate(df, height + 10)
In the same way, you can unquote values from the context if these
values represent a valid column. They must be either length 1 (they then
get recycled) or have the same length as the number of rows. In the
following example we create a new vector that we add to the data
frame:
var <- seq(1, nrow(df))
mutate(df, new = var)
A case in point is group_by(). While you might think it
has select semantics, it actually has mutate
semantics. This is quite handy as it allows to group by a modified
column:
group_by(starwars, sex)
group_by(starwars, sex = as.factor(sex))
group_by(starwars, height_binned = cut(height, 3))
This is why you can’t supply a column name to
group_by(). This amounts to creating a new column
containing the string recycled to the number of rows:
group_by(df, "month")
---
title: "My Introduction to the dplyr package"
author: "Arvind Venkatadri"
date: 06/July/2021
output:
  html_document:
    theme: flatly
    toc: TRUE
    toc_float: TRUE
    toc_depth: 2
    df_print: paged
    number_sections: TRUE
    code_folding: hide
    code_download: TRUE

---
## Instructions:

Start here if this is your first time using dplyr. You'll learn the basic philosophy, the most important data manipulation **verbs**, and the pipe, `%>%`, which allows you to combine multiple verbs together to solve real problems.

Read the text and execute each chunk as you go along. If you make any discoveries or have questions, put them into the text immediately after the chunk/text where your doubt is created. I have put in small instructions here and there for you to prompt your thinking and connection-making.

```{r, echo = FALSE, message = FALSE}
knitr::opts_chunk$set(collapse = T, comment = "#>")
options(tibble.print_min = 4L, tibble.print_max = 4L)
library(tidyverse)
set.seed(1014)

#devtools::install_github("gadenbuie/tidy-animated-verbs")

```

When working with data you must:

* Figure out what you want to do.

* Describe those tasks in the form of a computer program.

* Execute the program.

The `dplyr` package makes these steps fast and easy:

* By constraining your options, it helps you think about your data manipulation challenges.

* It provides simple **"verbs"**, functions that correspond to the most common data manipulation tasks, to help you translate your thoughts into code.

* It uses efficient backends, so you spend less time waiting for the computer. 

            
> Ne'er you mind about backends ;-) See Shakespeare's *[Hamlet.](https://www.goodreads.com/quotes/392818-there-s-a-divinity-that-shapes-our-ends-rough-hew-them-how)*

This document introduces you to dplyr's basic set of tools, and shows you how to apply them to data frames. `dplyr` also supports databases via the `dbplyr` package, once you've installed, read `vignette("dbplyr")` to learn more.

## Data: starwars

To explore the basic data manipulation verbs of `dplyr`, we'll use the dataset `starwars`. This dataset contains `r nrow(starwars)` characters and comes from the [Star Wars API](https://swapi.dev), and is documented in `?starwars` 

> This means: type `?starwars` in the Console. Try.

```{r}
dim(starwars)
starwars
```

Note that `starwars` is a `tibble`, a modern reimagining of the data frame. It's particularly useful for large datasets because it only prints the first few rows. You can learn more about tibbles at <https://tibble.tidyverse.org>; in particular you can convert data frames to tibbles with `as_tibble()`.

> Check your Environment Tab to inspect `starwars` in a separate tab.

## Single table verbs

`dplyr` aims to provide a function for each basic verb of data manipulation. These verbs can be organised into three categories based on the component of the dataset that they work with:

* Rows:
  * `filter()` chooses rows based on column values.
  * `slice()` chooses rows based on location.
  * `arrange()` changes the order of the rows.
  
* Columns:
  * `select()` changes whether or not a column is included.
  * `rename()` changes the name of columns.
  * `mutate()` changes the values of columns and creates new columns.
  * `relocate()` changes the order of the columns.

* Groups of rows:
  * `summarise()` collapses a group into a single row.

> Think of the parallels from Microsoft Excel. 


### The pipe

All of the `dplyr` functions take a data frame (or `tibble`) as the first argument. Rather than forcing the user to either save intermediate objects or nest functions, dplyr provides the `%>%` operator from `magrittr`. `x %>% f(y)` turns into `f(x, y)` so the result from one step is then "piped" into the next step. You can use the pipe to rewrite multiple operations that you can read left-to-right, top-to-bottom (**reading the pipe operator as "then"**). 

### Filter rows with `filter()`

`filter()` allows you to select a subset of rows in a data frame. Like all single verbs, the first argument is the tibble (or data frame). The second and subsequent arguments refer to variables within that data frame, selecting rows where the expression is `TRUE`.

For example, we can select all character with light skin color and brown eyes with:

> Note the double equal to sign (==) below! Equivalent to MS Excel Data -> Filter

```{r}
starwars %>% filter(skin_color == "light", eye_color == "brown")
```


### Arrange rows with `arrange()`

`arrange()` works similarly to `filter()` except that instead of filtering or selecting rows, it **reorders** them. It takes a data frame, and a set of column names (or more complicated expressions) to order by. If you provide more than one column name, each additional column will be used to break ties in the values of preceding columns:

```{r}
starwars %>% arrange(height, mass)
```

Use `desc()` to order a column in descending order:

```{r}
starwars %>% arrange(desc(height))
```

###  Choose rows using their position with `slice()`

`slice()` lets you index rows by their (integer) locations. It allows you to select, remove, and duplicate rows. 

>This is an important step in Prediction, Modelling and Machine Learning. 

We can get characters from row numbers 5 through 10.
```{r}
starwars %>% slice(5:10)
```

It is accompanied by a number of helpers for common use cases:

* `slice_head()` and `slice_tail()` select the first or last rows.

```{r}
starwars %>% slice_head(n = 3)
```

* `slice_sample()` randomly selects rows. Use the option prop to choose a certain proportion of the cases.

```{r}
starwars %>% slice_sample(n = 5)
starwars %>% slice_sample(prop = 0.1)
```

Use `replace = TRUE` to perform a bootstrap sample. If needed, you can weight the sample with the `weight` argument.

> `Bootstrap samples` are a special statistical sampling method. Counterintuitive perhaps, since you sample with replacement. Should remind you of your high school Permutation and Combination class, with all those urn models and so on. If you remember. 

* `slice_min()` and `slice_max()` select rows with highest or lowest values of a variable. Note that we first must choose  only the values which are not NA.

```{r}
starwars %>%
  filter(!is.na(height)) %>%
  slice_min(height, n = 3)
```

### Select columns with `select()`

Often you work with large datasets with many columns but only a few are actually of interest to you. `select()` allows you to rapidly zoom in on a useful subset using operations that usually only work on numeric variable positions:

```{r}
# Select columns by name
starwars %>% select(hair_color, skin_color, eye_color)
# Select all columns between hair_color and eye_color (inclusive)
starwars %>% select(hair_color:eye_color)
# Select all columns except those from hair_color to eye_color (inclusive)
starwars %>% select(!(hair_color:eye_color))
# Select all columns ending with color
starwars %>% select(ends_with("color"))
```

There are a number of helper functions you can use within `select()`, like `starts_with()`, `ends_with()`, `matches()` and `contains()`. These let you quickly match larger blocks of variables that meet some criterion. See `?select` for more details.

You can rename variables with `select()` by using named arguments:

```{r}
starwars %>% select(home_world = homeworld)
```

But because `select()` drops all the variables not explicitly mentioned, it's not that useful. Instead, use `rename()`:

```{r}
starwars %>% rename(home_world = homeworld)
```

### Add new columns with `mutate()`

Besides selecting sets of existing columns, it's often useful to add new columns that are functions of existing columns. This is the job of `mutate()`:

```{r}
starwars %>% mutate(height_m = height / 100)
```

We can't see the height in meters we just calculated, but we can fix that using a select command.

```{r}
starwars %>%
  mutate(height_m = height / 100) %>%
  select(height_m, height, everything())
```

`dplyr::mutate()` is similar to the base `transform()`, but allows you to refer to columns that you've just created:

```{r}
starwars %>%
  mutate(
    height_m = height / 100,
    BMI = mass / (height_m^2)
  ) %>%
  select(BMI, everything())
```

If you only want to keep the new variables, use `transmute()`:

```{r}
starwars %>%
  transmute(
    height_m = height / 100,
    BMI = mass / (height_m^2)
  )
```

### Change column order with `relocate()`

Use a similar syntax as `select()` to move blocks of columns at once

```{r}
starwars %>% relocate(sex:homeworld, .before = height)
```


### Summarise values with `summarise()`

The last verb is `summarise()`. It collapses a data frame to a single row.

```{r}
starwars %>% summarise(mean_height = mean(height, na.rm = TRUE))
```

It's not that useful until we learn the `group_by()` verb below.


### Commonalities

You may have noticed that the syntax and function of all these verbs are very similar:

* The first argument is a data frame.

* The subsequent arguments describe what to do with the data frame. You can
  refer to columns in the data frame directly without using `$`.

* The result is a new data frame

Together these properties make it easy to chain together multiple simple steps to achieve a complex result. 

These five functions provide the basis of a language of data manipulation. At the most basic level, you can only alter a tidy data frame in five useful ways: you can reorder the rows (`arrange()`), pick observations and variables of interest (`filter()` and `select()`), add new variables that are functions of existing variables (`mutate()`), or collapse many values to a summary (`summarise()`). 

## Combining functions with `%>%` 

The dplyr API is functional in the sense that function calls don't have side-effects. You must always save their results. This doesn't lead to particularly elegant code, especially if you want to do many operations at once. You either have to do it step-by-step:

```{r, eval = FALSE}
a1 <- group_by(starwars, species, sex)
a2 <- select(a1, height, mass)
a3 <- summarise(a2,
  height = mean(height, na.rm = TRUE),
  mass = mean(mass, na.rm = TRUE)
)
```

Or if you don't want to name the intermediate results, you need to wrap the function calls inside each other:

```{r}
summarise(
  select(
    group_by(starwars, species, sex),
    height, mass
  ),
  height = mean(height, na.rm = TRUE),
  mass = mean(mass, na.rm = TRUE)
)
```

This is difficult to read because the order of the operations is from inside to out. Thus, the arguments are a long way away from the function. To get around this problem, dplyr provides the `%>%` operator from magrittr. `x %>% f(y)` turns into `f(x, y)` so you can use it to rewrite multiple operations that you can read left-to-right, top-to-bottom (reading the pipe operator as "then"):

```{r}
starwars %>%
  group_by(species, sex) %>%
  summarise(
    mean_height = mean(height, na.rm = TRUE),
    mean_mass = mean(mass, na.rm = TRUE)
  )
```

## Patterns of operations

The dplyr verbs can be classified by the type of operations they accomplish (we sometimes speak of their **semantics**, i.e., their meaning). It's helpful to have a good grasp of the difference between select and mutate operations.

### Selecting operations

One of the appealing features of dplyr is that you can refer to columns from the tibble as if they were regular variables. However, the syntactic uniformity of referring to bare column names hides semantical differences across the verbs. A column symbol supplied to `select()` does not have the same meaning as the same symbol supplied to `mutate()`.

Selecting operations expect column names and positions. Hence, when you call `select()` with bare variable names, they actually represent their own positions in the tibble. The following calls are completely equivalent from dplyr's point of view:

```{r}
# `name` represents the integer 1
select(starwars, name)
select(starwars, 1)
```

By the same token, this means that you cannot refer to variables from the surrounding context if they have the same name as one of the columns. In the following example, `height` still represents 2, not 5:

```{r}
height <- 5
select(starwars, height)
```

One useful subtlety is that this only applies to bare names and to selecting calls like `c(height, mass)` or `height:mass`. In all other cases, the columns of the data frame are not put in scope. This allows you to refer to contextual variables in selection helpers:

```{r}
name <- "color"
select(starwars, ends_with(name))
```

These semantics are usually intuitive. But note the subtle difference:

```{r}
name <- 5
select(starwars, name, identity(name))
```

In the first argument, `name` represents its own position `1`. In the second argument, `name` is evaluated in the surrounding context and represents the fifth column.


### Mutating operations

Mutate semantics are quite different from selection semantics. Whereas `select()` expects column names or positions, `mutate()` expects *column vectors*.  We will set up a smaller tibble to use for our examples.

```{r}
df <- starwars %>% select(name, height, mass)
```

When we use `select()`, the bare column names stand for their own positions in the tibble. For `mutate()` on the other hand, column symbols represent the actual column vectors stored in the tibble. Consider what happens if we give a string or a number to `mutate()`:

```{r}
mutate(df, "height", 2)
```

`mutate()` gets length-1 vectors that it interprets as new columns in the data frame. These vectors are recycled so they match the number of rows. That's why it doesn't make sense to supply expressions like `"height" + 10` to `mutate()`. This amounts to adding 10 to a string!
The correct expression is:

```{r}
mutate(df, height + 10)
```

In the same way, you can unquote values from the context if these values represent a valid column. They must be either length 1 (they then get recycled) or have the same length as the number of rows. In the following example we create a new vector that we add to the data frame:

```{r}
var <- seq(1, nrow(df))
mutate(df, new = var)
```

A case in point is `group_by()`. While you might think it has `select` semantics, it actually has `mutate` semantics. This is quite handy as it allows to group by a modified column:

```{r}
group_by(starwars, sex)
group_by(starwars, sex = as.factor(sex))
group_by(starwars, height_binned = cut(height, 3))
```

This is why you can't supply a column name to `group_by()`. This amounts to creating a new column containing the string recycled to the number of rows:

```{r}
group_by(df, "month")
```


## Two table verbs

Sometimes our data is spread across more than one table. Often these tables are *linked* by some common, or common-looking, variable columns. `dplyr` allows us to work with such data that is spread over more than one table. 
More information is available here: [Two Table Verbs in dplyr](https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html)

The operations/verbs used to manipulate two-table verbs are:

* Mutating joins, which add new variables to one table from matching rows in another.
  - `inner_join()`
  
```{r,echo=FALSE}
knitr::include_graphics("images/inner-join.gif")
```

  - `left_join()`
  
```{r,echo=FALSE}
knitr::include_graphics("images/left-join.gif")
```

  - `right_join()`
  
```{r,echo=FALSE}
knitr::include_graphics("images/right-join.gif")
```


  - `full_join()`
    
```{r,echo=FALSE}
knitr::include_graphics("images/full-join.gif")
```

  
* Filtering joins, which filter observations from one table based on whether or not they match an observation in the other table.
  - `semi_join(x, y)` keeps all observations in x that have a match in y.
  
```{r,echo=FALSE}
knitr::include_graphics("images/semi-join.gif")
```


  - `anti_join(x, y)` drops all observations in x that have a match in y.
  
```{r,echo=FALSE}
knitr::include_graphics("images/anti-join.gif")
```




* Set operations, which combine the observations in the data sets as if they were set elements.

  - union()
  
```{r,echo=FALSE}
knitr::include_graphics("images/union.gif.")
```

  - union_all(), 
  
```{r,echo=FALSE}
knitr::include_graphics("images/union-all.gif.")
```

  - intersect(), 
  
```{r,echo=FALSE}
knitr::include_graphics("images/intersect.gif")
```
  
  - setdiff()
  
```{r,echo=FALSE}
knitr::include_graphics("images/setdiff-rev.gif")
```

* Tidyr Operations: 
- pivot_longer()
- pivot_wider()
  
```{r,echo=FALSE}
knitr::include_graphics("images/tidyr-pivoting.gif")
```

